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Abstract Eliminating conventional pulsed B0-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.

In complex data environments, rational handling of unbalanced datasets is key to improving the reliability of early disease prediction. Early warning of disease risk in both temporal and spatial terms, contributes to disease prevention and treatment. To this end, a bi-dimensional substratum information mining model based on Association Rule Digging with Dynamic Thresholding and Weight Optimization (ARDdtwo) was proposed for the early diagnosis of thyroid cancer. It is an integrated assessment framework consisting of association rule digging by constructing a dynamic threshold model (ADRcdt) for qualitative analysis, and a self-optimizing component importance measurement model (SoCIM) for quantitative analysis. ARDcdt incorporates temporal and spatial features of sparse data to address the distributional bias problem. Moreover, new importance diagnostic calculations were designed to further identify high-risk low-frequency (HRLF). The SoCIM can determine the relative weight of each component by assessing its level of risk in the overall system based on the Risk Enhancement Level (REL) and Risk Reduction Level (RRL), realizing the self-adjustment and optimization of the weight setting. Finally, the model was validated through an empirical analysis. The evaluation of the research work shows that improved results were achieved, such as accuracy, f1-score, and precision, with optimized values of 36.04%,56.57%, and 53.89%, respectively. The overall area under the curve for the model was 0.882. This proves the validity of the proposed model for practical applications. For patients, it can simplify the pathological process and reduce the examination costs.

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

Strain clamps are critical components in high-voltage overhead transmission lines, and detection of their defects becomes an important part of regular inspection of transmission lines. A dual UAV (unmanned aerial vehicle) system was proposed to detect strain clamps in multiple split-phase conductors. The main UAV was equipped with a digital radiography (DR) imaging device, a mechanical arm, and an edge intelligence module with visual sensors. The slave UAV was equipped with a digital imaging board and visual sensors. A workflow was proposed for this dual UAV system. Target detection and distance detection of the strain clamps, as well as detection of the defects of strain clamps in DR images, are the main procedures of this workflow. To satisfy the demands of UAV-borne and real-time deployment, the improved YOLOv8-TR algorithm was proposed for the detection of strain clamps (the mAP@50 was 60.9%), and the KD-ResRPA algorithm is used for detecting defects in DR images (the average AUCROC of the three datasets was 82.7%). Field experiments validated the suitability of our dual UAV-based system for charged detection of strain clamps in double split-phase conductors, demonstrating its potential for practical application in live detecting systems.

Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep learning solutions, particularly convolutional neural networks (CNNs), represent the state of the art (SOTA) for CD. However, CNN-based models require substantial amounts of annotated data, which can be both expensive and time-consuming. Conversely, acquiring a large volume of unannotated images is relatively easy. Recently, self-supervised contrastive learning has emerged as a promising method for learning from unannotated images, thereby reducing the need for annotation. However, most existing methods employ random values or ImageNet pre-trained models to initialize their encoders and lack prior knowledge tailored to the demands of CD tasks, thus constraining the performance of CD models. To address these challenges, we introduce a novel feature-differencing-based framework called Barlow Twins for self-supervised pre-training and fine-tuning in CD (BTCD). The proposed approach employs absolute feature differences to directly learn unique representations associated with regions that have changed from unlabeled bi-temporal remote sensing images in a self-supervised manner. Moreover, we introduce invariant prediction loss and change consistency regularization loss to enhance image alignment between bi-temporal images in both the decision and feature space during network training, thereby mitigating the impact of variation in radiation conditions, noise, and imaging viewpoints. We select the improved UNet++ model for fine-tuning self-supervised pre-training models and conduct experiments using two publicly available LULC CD datasets. The experimental results demonstrate that our proposed approach outperforms existing SOTA methods in terms of competitive quantitative and qualitative performance metrics.

This study investigated the effects of ultrasound–assisted immersion freezing (UIF) at different power rates (0, 200, 400, and 600 W) on the changes in beef quality and flavor after braising. The results demonstrated that UIF treatment at 400 W significantly reduced the juice loss (cooking loss decreased from 49.04% to 39.74%) and fat oxidation (TBARS value decreased from 0.32 mg/kg to 0.20 mg/kg) of braised beef. In addition, the tenderness (hardness value decreased from 5601.50 g to 2849.46 g) and color stability of braised beef were improved after UIF treatment. The flavor characteristics of braised beef were characterized using an electronic nose and an electronic tongue. The PCA analysis data showed that the cumulative contribution rates of the first and second principal components were 85% and 93.2%, respectively, with the first principal component accounting for a higher proportion. The UIF–400 W group had the highest concentration for the first principal component, and the differentiation was not significant compared to the control group. The total amino acid values of different power UIF treatment groups were improved compared to the AF treatment group, indicating that UIF can effectively reduce the losses caused by freezing. The results demonstrate that ultrasound–assisted freezing treatment is beneficial in enhancing the tenderness and flavor attributes of beef after braising, providing new insights into the processing of meat products with desirable quality characteristics.

Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning.

Radar observation is an effective way to understand subsurface structures in terms of the dielectric constant, whose controlling factors include chemical composition, packing density, and water/ice content. Recently, laboratory measurements have shown that the dielectric constant of lunar regolith simulants also depends on the temperature, which has never been evaluated from remote sensing data. In this study, we estimated the dielectric constant from the Miniature Radio Frequency (Mini-RF) data on a lunar crater floor in the north polar region at two different local times (i.e., different surface temperatures). We calculated the dielectric constant using the inversion method and obtained the bolometric surface temperature from the Diviner Lunar Radiometer Experiment (Diviner) data. The histograms of the estimated dielectric constant values are different between the two local times. This could be interpreted as a result of the temperature dependence of the dielectric constant, while further evaluation of the influence of topography on the incidence angle and small surface roughness is needed. Nevertheless, our result suggests that the temperature dependence of the dielectric constant should be considered when interpreting S-band radar observations of the Moon and other celestial bodies with large surface temperature differences.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

To ensure a stable and reliable power supply, the valid and timely response of protective relays are indispensable. Through the prevention of fault expansions, potential equipment damage or system collapse can be averted, where their setting is one vital prerequisite for such effective implementations. However, the increasing complexity of distribution power systems results in more challenges for protection tuning strategies. Ergo, this paper presents an ensemble that combines the independent factor evaluation (IFE) and quantum genetic optimization (QGO) models to further optimize the performance of relays according to their distributed tuning environment. In this ensemble, both near and far-end fault characteristics can be incorporated. In the first stage, the IFE dimensional reduction model is deployed for massive heterogeneous input data, where the statistical independence of input signals is calculated, the linear transformation matrix to decouple mixed signals is found, the linear combination of such signals is formed, and the non-Gaussian property to sort them is established. This can ameliorate the following calculation efficiency under those high-dimensional data scenarios. Subsequently, the QGO model is designed to further improve relay settings, where qubit representation is built to reduce required chromosomes, the linear superposition of the optimal solution probability in different states is implemented for a better diversity and convergence performance, and a self-adaption quantum gate is established to dynamically update the qubit chromosome groups and two-state solution combinations. Lastly, an empirical case study is presented, which validates the enhanced convergence, accuracy, and rapidity of the proposed ensemble.

The penetration rate of intermittent renewable energy in power system is gradually increasing, which brings challenges to the optimal dispatch of power system. To solve these problems, a multi-regional interconnected transmission network optimization method based on analytical target cascading is proposed. Firstly, the reactive power regulation characteristics of renewable energy is investigated, and the models of wind turbine and photovoltaic generation are established. Secondly, the power system is decomposed into multiple sub-systems using bus tearing method, and an optimal transmission switching (OTS) model with renewable energies is established. Then, the analytical target cascading (ATC) approach is employed to decompose the model into the main problem and several sub-problems for parallel computation to achieve coordinated optimization of the complex transmission network. Finally, the IEEE 14-bus system and the IEEE 118-bus system are used to verify the proposed model. The results show that the proposed method can deal with the coupling nonlinear problem well and promote the consumption of renewable energy.

The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.

Abstract An artificial potential field method based on global path guidance (G‐APF) is proposed for target unreachability and local minima problems of the conventional artificial potential field (APF) method in complex environments with dynamic obstacles. First, for the target unreachability problem, the global path attraction is added to the APF; second, an obstacle detection optimisation method is proposed and the optimal virtual target point is selected by setting the evaluation function to improve the local minima problem; finally, based on the obstacle detection optimisation method, the gravitational and repulsive processes are improved so that the path can pass through the narrow channel smoothly and remain collision‐free. Experiments show that the method optimises 40.8% of the total path corners, reduces 81.8% of the number of path oscillations, and shortens 4.3% of the path length in Map 1. It can be applied to the vehicle obstacle avoidance path planning problem in complex environments with dynamic obstacles.

The utility of interleaved odd-number leg birdcage coils is demonstrated for decoupling in double- and triple-tuned multinuclear applications. The birdcage was designed to geometrically decouple from a planar double-tuned (1H-23Na) array and from a 31P saddle coil insert to create a triple-tuned configuration. Comparisons between an actively detuned coil and a purely geometrically decoupled architecture were used to demonstrate the capabilities of the design. In particular cases, the simplicity and adaptability of the interleaved nine-leg design for multinuclear nuclear magnetic resonance (NMR) offer a straightforward alternative to the often complex and lossy designs currently available for multinuclear birdcages and volume coils.

Extensive research has demonstrated the advantageous utilization of medium–low temperature fast pyrolysis (FP) for biomass, yielding high–grade liquid–phase chemicals or fuels. However, the field of FP–based high–performance solid biochar research still presents several gaps. Herein, a one–step versus two–step method for biomass H3PO4 activation under FP was comparatively analyzed for the first time, and efficiently activated carbons (ACs) for dye removal were successfully synthesized at a low temperature (723 K). Investigation of methylene blue (MB) adsorption revealed that the one–step sample P–H–0.5, possessing a specific surface area of 1004 m2·g−1, exhibited a remarkable adsorption capacity of 695.54 mg·g−1 with an ultra–high removal rate (99.94%, C0 = 150 mg·L−1). The two–step sample P–2–H–2, a modified byproduct of FP, achieved efficient dye adsorption in the shortest time (2 min, 383.91 mg·g−1). This originated from the well–developed surface macropores and elevated group content derived from phosphorus (P)—modification. Both adsorption data were well–fitted with pseudo–second–order kinetics and the Langmuir model, revealing the presence of chemical effects and the dominance of monolayer adsorption. A more detailed kinetic study suggested intrapore transport primarily governed the adsorption process on P–H–0.5, whereas P–2–H–2 relied on surface diffusion. FTIR and XPS revealed notable differences in the active sites between the two methods. Aside from –OH, –COOH with C–O–P, the P elements of P–H–0.5 were classified as C–P–O3 and C2–O–P2, demonstrating the ability of one–step FP to introduce heteroatoms into carbon defects. The basic interactions of ACs with MB were π–π stacking and hydrogen bonding established by –OH–containing groups. At a suitable pH (>5), most H+ was removed from the surface, and the electrostatic attraction became the strongest linking force. Both ACs exhibited exceptional reusability, with removal rates surpassing 90% of the initial rate after four cycles of regeneration.

As one indispensable part of power systems, the reliable-operated power transformers are vital for energy transmission, whereas they are remarkably threatened by potential fault events. To achieve the satisfying and valid operation of power transformers, any fault events that may impact their health ought to be evaluated and early warned. With such motivations, this paper presents original insights on the assessment of power transmission health states via their internal dissolved gas, and an enhanced Association Rule Mining (ARM) model incorporating the analysis of High-Impact-Low-Probability (HILP) components, as well as a dynamic fault event risk evaluation approach, is proposed. The first step is to differentiate the risky components. Unlike the standard ARM, the rarely occurred components in each feature can also be assessed explicitly as the common components to explore the underlying HILP components in the proposed model, rather than just being viewed as trivial data and directly omitted. The second step is to rate the risk level of each risky component. A component importance measure-based evaluation approach is deployed to assess the corresponding risk weights of distinguished risky components. In this approach, the risk weight is determined straightforwardly via the impacts of each component on the variation level of total risks in the system, rather than simply by its frequency of occurrence or data share. Finally, the parameters of the risk weight evaluation approach can be dynamically adapted in an adjustment framework as well. This model is testified through an empirical case study, and the leading results can demonstrate its flexibility and robustness during real applications.

Spectrum sensing is an indispensable technology for cognitive radio networks, which enables secondary users (SUs) to discover spectrum holes and to opportunistically use under-utilized channels without causing interference to primary users. Aim at improving the sensing performance, a multi-antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering (MIEGAC) is proposed in this paper. Specifically, in order to reduce the amount of signal that is transferred to the fusion center, an information pre-processing scheme based on principal component analysis (PCA) is presented. Main information from the sensing signal is extracted via PCA, which reduces the cost of the reporting channel and the impact of interfering information on detection result. Furthermore, an information fusion method is described in this paper, which takes the place of complicated matrix decomposition algorithms. Moreover, inspired by machine learning, a clustering scheme based on genetic algorithm is introduced to classify signal features, which implements the spectrum sensing decision and avoids calculating the decision threshold. Simulation results illustrate that the MIEGAC can considerably improve the sensing performance for spectrum sensing. Significantly, this paper provides a novel approach for the design of centralized spectrum sensing algorithms in cognitive radio technologies.

Passive films were formed on A508-3 steel (A508-3), 304L stainless steel (304L) and Incoloy 800 (In800) with blank/zinc/zinc–aluminum treatments in air at 300 °C. The electrochemical corrosion behaviors of different metals were investigated through potentiodynamic polarization, electrochemical impedance spectroscopy and the Mott–Schottky technique. The morphology and composition of passive films were analyzed using scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS). The zinc–aluminum treatment effectively reduced the corrosion current and increased the impedance of A508-3 and 304L, but had a weak effect on In800. The zinc–aluminum treatment reduced the carrier concentration of A508-3 and changed the semiconductor property of 304L and In800. The order of zinc–aluminum treatment for improving the corrosion resistance of three metals was: A508-3 > 304L > In800. In addition, the zinc–aluminum treatment enhanced the density and smoothness of passive films. According to the composition analysis, spinel ZnAl2O4 was formed on three metals; however, the amount of spinel varied with the content of nickel and chromium in different metals, which affected the results of using this technology.

The High-Speed Power Line Carrier Communication (HPLC) enables the connections among power metering devices in integrated energy systems, and thus their satisfying operations are indispensable for system reliabilities. In order to more precisely diagnose their conditions especially in real complex data scenes, a multi-model evaluation ensemble is proposed in this paper. Firstly, typical IoT application contexts of customer-side metering equipment are analyzed, thus the corresponding main impact factors along with their performance evaluation indices can be probed. Next, to handle the multi-source, heterogeneous, high-dimensional datasets during applications, the Kernel Independent Component Analysis (KICA) is established to diminish data dimensionalities, thus the individual weights of each index can be rated. On the other hand, the Component Importance Measure (CIM) model is built to differentiate the impact degree of each indicator on the overall IoT connection performance, where the influence of dissimilar index on the entire performance, rather than the proportion or frequency, will be directly assessed to determine their impact weights. Ergo, a comprehensive diagnosis can be achieved via these two-fold total weights accordingly. Finally, the feasibility and effectiveness of the proposed method can be verified by an empirical case study, which is conducive to further improving the accuracy and rationality of HPLC condition evaluations. [ABSTRACT FROM AUTHOR]